Improving Outpatient Psychiatric Appointment Attendance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
abstract: Mental health issues are a growing concern for individuals and the public. When patients do not attend their mental health appointments they place themselves at risk for poor health outcomes including worsening of symptoms, relapse, hospitalization, or danger to self and other behaviors. The breadth, background, and significance of this issue were investigated to determine a clinically relevant PICOT question. These elements of the PICOT question were investigated and high-quality evidence was gathered, analyzed, and synthesized in order to develop recommendations for an evidence-based project to help with no-shows at a non-profit integrated healthcare organization that is experiencing a high incidence of no-shows. The Quality Health Outcomes Model and Ottawa Model of Research Use guide the implementation and monitoring of the project. A chart review was completed in order to understand the impact of a novel automated reminder system on the no-show rate for all psychiatric appointments for 18 months. Additionally, demographic and appointment information was gathered to identify trends in the data and factors related to appointment status. The no-show rate significantly increased in 2019 with the new reminder system. No-shows occurred significantly more in males, tele-medicine appointments, and hospital discharge appointments. There were significant differences in no-show rates observed between reported races, with different providers, and at different practice locations. This gap analysis has provided insight into further projects and work to be completed in order to decrease no-shows, improve treatment compliance, produce better health outcomes, and increase revenue for this organization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it